Design of Network Intrusion Detection System Using Lion Optimization-Based Feature Selection with Deep Learning Model

被引:3
|
作者
Alghamdi, Rayed [1 ]
机构
[1] King Abdulaziz Univ, Fac Comp & Informat Technol, Dept Informat Technol, Jeddah 21589, Saudi Arabia
关键词
network intrusion detection system; network security; lion optimization algorithm; feature selection; deep learning; 68-11;
D O I
10.3390/math11224607
中图分类号
O1 [数学];
学科分类号
0701 ; 070101 ;
摘要
In the domain of network security, intrusion detection systems (IDSs) play a vital role in data security. While the utilization of the internet amongst consumers is increasing on a daily basis, the significance of security and privacy preservation of system alerts, due to malicious actions, is also increasing. IDS is a widely executed system that protects computer networks from attacks. For the identification of unknown attacks and anomalies, several Machine Learning (ML) approaches such as Neural Networks (NNs) are explored. However, in real-world applications, the classification performances of these approaches are fluctuant with distinct databases. The major reason for this drawback is the presence of some ineffective or redundant features. So, the current study proposes the Network Intrusion Detection System using a Lion Optimization Feature Selection with a Deep Learning (NIDS-LOFSDL) approach to remedy the aforementioned issue. The NIDS-LOFSDL technique follows the concept of FS with a hyperparameter-tuned DL model for the recognition of intrusions. For the purpose of FS, the NIDS-LOFSDL method uses the LOFS technique, which helps in improving the classification results. Furthermore, the attention-based bi-directional long short-term memory (ABiLSTM) system is applied for intrusion detection. In order to enhance the intrusion detection performance of the ABiLSTM algorithm, the gorilla troops optimizer (GTO) is deployed so as to perform hyperparameter tuning. Since trial-and-error manual hyperparameter tuning is a tedious process, the GTO-based hyperparameter tuning process is performed, which demonstrates the novelty of the work. In order to validate the enhanced solution of the NIDS-LOFSDL system in terms of intrusion detection, a comprehensive range of experiments was performed. The simulation values confirm the promising results of the NIDS-LOFSDL system compared to existing DL methodologies, with a maximum accuracy of 96.88% and 96.92% on UNSW-NB15 and AWID datasets, respectively.
引用
收藏
页数:17
相关论文
共 50 条
  • [31] Enhanced Chimp Optimization-Based Feature Selection with Fuzzy Logic-Based Intrusion Detection System in Cloud Environment
    Alohali, Manal Abdullah
    Elsadig, Muna
    Al-Wesabi, Fahd N. N.
    Al Duhayyim, Mesfer
    Hilal, Anwer Mustafa
    Motwakel, Abdelwahed
    APPLIED SCIENCES-BASEL, 2023, 13 (04):
  • [32] Network intrusion detection system for IoT security using machine learning and statistical based hybrid feature selection
    Walling, Supongmen
    Lodh, Sibesh
    SECURITY AND PRIVACY, 2024, 7 (06):
  • [33] A Network Intrusion Detection System Using Hybrid Multilayer Deep Learning Model
    Umair, Muhammad Basit
    Iqbal, Zeshan
    Faraz, Muhammad Ahmad
    Khan, Muhammad Attique
    Zhang, Yu-Dong
    Razmjooy, Navid
    Kadry, Sefedine
    BIG DATA, 2024, 12 (05) : 367 - 376
  • [34] Network intrusion detection method based on deep learning feature extraction
    Song Y.
    Hou B.
    Cai Z.
    Huazhong Keji Daxue Xuebao (Ziran Kexue Ban)/Journal of Huazhong University of Science and Technology (Natural Science Edition), 2021, 49 (02): : 115 - 120
  • [35] Network Intrusion Detection System using Feature Extraction based on Deep Sparse Autoencoder
    Lee, Joohwa
    Pak, JuGeon
    Lee, Myungsuk
    11TH INTERNATIONAL CONFERENCE ON ICT CONVERGENCE: DATA, NETWORK, AND AI IN THE AGE OF UNTACT (ICTC 2020), 2020, : 1282 - 1287
  • [36] Intrusion Detection System using Bayesian Network and Feature Subset Selection
    Jabbar, M. A.
    Aluvalu, Rajanikanth
    Reddy, S. Sai Satyanarayana
    2017 IEEE INTERNATIONAL CONFERENCE ON COMPUTATIONAL INTELLIGENCE AND COMPUTING RESEARCH (ICCIC), 2017, : 640 - 644
  • [37] Red fox optimizer based feature selection with optimal deep learning based Intrusion detection for network security
    Sunkara S.
    Suresh T.
    Sathiyasuntharam V.
    International Journal of Information Technology, 2023, 15 (8) : 4437 - 4447
  • [38] An anomaly-based Network Intrusion Detection System using Deep learning
    Nguyen Thanh Van
    Tran Ngoc Thinh
    Le Thanh Sach
    2017 INTERNATIONAL CONFERENCE ON SYSTEM SCIENCE AND ENGINEERING (ICSSE), 2017, : 210 - 214
  • [39] Majority Voting and Feature Selection Based Network Intrusion Detection System
    Patil, Dharmaraj R.
    Pattewar, Tareek M.
    EAI ENDORSED TRANSACTIONS ON SCALABLE INFORMATION SYSTEMS, 2022, 9 (06):
  • [40] Research on Feature Selection Method of Intrusion Detection Based on Deep Belief Network
    BaoyiWang
    Sun, Shan
    Zhang, Shaomin
    PROCEEDINGS OF THE 2015 3RD INTERNATIONAL CONFERENCE ON MACHINERY, MATERIALS AND INFORMATION TECHNOLOGY APPLICATIONS, 2015, 35 : 556 - 561